Digital image enhancement is no longer a new field of endeavor, but many long-standing problems remain unsolved. Some of these problems appear inherent and therefore incapable of solution. For example, images contain data on portions of the objects being studied that are of interest, portions of the objects that are not of interest, and noise. Specifically in digital fluorography the images include data on the vascular system that is of interest. Data is also present on portions of the body that are not of interest. Unfortunately, noise is also present in the images. The images are enhanced by filtering and subtraction techniques that are designed to:
1. Remove from the images the portions of the patient's anatomy that are not of interest; PA1 2. Increase the signal to noise ratio (SNR); and PA1 3. Enhance the outlines of the portions of the anatomy that are of interest. PA1 acquiring an image in the form of data, said image comprising noise, "points" and edges; PA1 filtering to attenuate the "points" and the noise relative to the edges; and PA1 optimally enhancing the long edges.
In digital fluorography the removal from the image of portions of the patient's anatomy that are not of interest is accomplished by subtraction or by temporal filtering. The results are images wherein the data of interest comprises mainly long-edged items and noise. The enhancement of the outlines of the portions of the anatomy that are of interest thus involves the enhancement of long edges.
High pass filters enhance transitions. Therefore using a high pass filter on an image will enhance both the edges or outlines of objects and the noise. It should be noted that whereas the "information content" of an image decreases with increasing frequencies, the "noise content" of "white" noise, such as quantum noise (e.g. proton counting errors) is constant with one-dimensional frequencies and rises linearly with two-dimensional frequencies of images. The enhancing filters thus generally are "cut-off" at some frequencies to keep from increasing the noise of high frequencies where there is essentially little or no information.
It is known that the frequency responses of "points" and "lines" are inherently different. Thus the responses of "points" and "lines" to the same filters are different. For example, with "gaussian"-type low-pass filters the cut-off frequency of "points" is lower than the cut-off frequency of "lines". Also the declining slope of the filter response to "points" is steeper than the declining slope of the filter response to "lines". Certain types of high-pass filters such as "gradient-squared"-type filters also exhibit different responses to "points" and to "lines". Here the response to "points" increases linearly at a greater slope vs. frequency than does the response to "lines".
In the past the filters have not been tailored to differentiate and distinguish between "points" (which generally includes noise) and long "lines" or edges--regardless of the orientation of the edges. It is possible to "fine tune" the filters to optimize the parameters so that the points are filtered out and the SNR of the long edges is improved. Such filter optimization has not been accomplished in the prior art filters presently used with digital fluorographic systems.
There is a need for filters in digital fluorographic systems that have parameters optimized to attenuate the "points" and to improve the SNR of long edges. Similarly, there is a need for such filters that will not smear the edges in the images.